By Topic

Adaptive Weighted Nearest Feature Space Analysis and Its Application to Feature Extraction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Lijun Yan ; Shenzhen Grad. Sch., Harbin Inst. of Technol., Harbin, China ; Cong Wang ; Jeng-Shyang Pan

In this paper, a new feature extraction algorithm named Adaptive Weighted Nearest Feature Space Analysis (AWNFSA) is proposed. AWNFSA is a Nearest Feature Space (NFS) based subspace learning approach. In Discriminant Nearest Feature Space Analysis (DNFSA) algorithm based on NFS, it may lead the result into misclassification when the between class scatter is very big or within class scatter is very small. Different from DNFSA, AWNFSA evaluates the effect of two scatter for classification through choosing their weights adaptively. The proposed AWNFSA is applied to image classification on ORL face Database. The experimental results demonstrate the efficiency of the proposed AWNFSA.

Published in:

2012 Conference on Technologies and Applications of Artificial Intelligence

Date of Conference:

16-18 Nov. 2012